UNDERSTANDING AND EDA OF 7+ MILLION COMPANY DATASET¶

image.png

In [1]:
#from google.colab import drive
#drive.mount('/content/drive')
In [2]:
import pandas as pd 
import numpy as np
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
In [3]:
df = pd.read_csv('companies_sorted.csv')
df
#reading data to variable df
Out[3]:
Unnamed: 0 name domain year founded industry size range locality country linkedin url current employee estimate total employee estimate
0 5872184 ibm ibm.com 1911.0 information technology and services 10001+ new york, new york, united states united states linkedin.com/company/ibm 274047 716906
1 4425416 tata consultancy services tcs.com 1968.0 information technology and services 10001+ bombay, maharashtra, india india linkedin.com/company/tata-consultancy-services 190771 341369
2 21074 accenture accenture.com 1989.0 information technology and services 10001+ dublin, dublin, ireland ireland linkedin.com/company/accenture 190689 455768
3 2309813 us army goarmy.com 1800.0 military 10001+ alexandria, virginia, united states united states linkedin.com/company/us-army 162163 445958
4 1558607 ey ey.com 1989.0 accounting 10001+ london, greater london, united kingdom united kingdom linkedin.com/company/ernstandyoung 158363 428960
... ... ... ... ... ... ... ... ... ... ... ...
7173421 1494427 certiport vouchers certiportvouchers.com 2011.0 information technology and services 1 - 10 NaN NaN linkedin.com/company/certiport-vouchers 0 1
7173422 1494429 black tiger fight club blacktigerclub.com 2006.0 health, wellness and fitness 1 - 10 peking, beijing, china china linkedin.com/company/black-tiger-club-hero 0 6
7173423 4768462 catholic bishop of chicago NaN NaN religious institutions 1 - 10 inverness, illinois, united states united states linkedin.com/company/catholic-bishop-of-chicago 0 1
7173424 1494436 medexo robotics ltd NaN NaN research 1 - 10 london, london, united kingdom united kingdom linkedin.com/company/medexo-robotics-ltd 0 2
7173425 2842728 dynamic controls solutions inc dynamiccontrolssolutions.com NaN automotive 1 - 10 wixom, michigan, united states united states linkedin.com/company/dynamic-controls-solution... 0 1

7173426 rows × 11 columns

Choosing the necessary features¶

1.name 2.industry 3.country 4.size range 5.current employee estimated 6.total employee estimated 7.year founded

In [4]:
df = df[['name','industry','country','size range','current employee estimate','total employee estimate','year founded']]
df.head()
Out[4]:
name industry country size range current employee estimate total employee estimate year founded
0 ibm information technology and services united states 10001+ 274047 716906 1911.0
1 tata consultancy services information technology and services india 10001+ 190771 341369 1968.0
2 accenture information technology and services ireland 10001+ 190689 455768 1989.0
3 us army military united states 10001+ 162163 445958 1800.0
4 ey accounting united kingdom 10001+ 158363 428960 1989.0
In [5]:
df.info()
#info function discribes Dtypes of the particular feature

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7173426 entries, 0 to 7173425
Data columns (total 7 columns):
 #   Column                     Dtype  
---  ------                     -----  
 0   name                       object 
 1   industry                   object 
 2   country                    object 
 3   size range                 object 
 4   current employee estimate  int64  
 5   total employee estimate    int64  
 6   year founded               float64
dtypes: float64(1), int64(2), object(4)
memory usage: 383.1+ MB
In [6]:
df.describe()
#describe function only returns the count,mean,std, of numerical feature
Out[6]:
current employee estimate total employee estimate year founded
count 7.173426e+06 7.173426e+06 3.566446e+06
mean 1.387121e+01 3.225438e+01 2.001747e+03
std 3.545919e+02 8.741556e+02 2.096627e+01
min 0.000000e+00 1.000000e+00 1.451000e+03
25% 1.000000e+00 1.000000e+00 1.999000e+03
50% 1.000000e+00 2.000000e+00 2.009000e+03
75% 4.000000e+00 7.000000e+00 2.013000e+03
max 2.740470e+05 7.169060e+05 2.103000e+03

Checking the missing values¶

In [7]:
df.isnull().sum()
#isnull function will return if the features are null(empty)in boolean(True or False)
#sum() will return no of counts.
Out[7]:
name                               3
industry                      290003
country                      2349207
size range                         0
current employee estimate          0
total employee estimate            0
year founded                 3606980
dtype: int64

Calculating the percentage of missing values

In [8]:
#df.isnull().mean() 
#or 
pd.set_option('display.max_rows',10)
#set_option will let us to decide, display,max_rows,'10' can choose no of rows i want to see
df.isnull().mean().sort_values()
#sort_values sort it in ascending order
Out[8]:
size range                   0.000000e+00
current employee estimate    0.000000e+00
total employee estimate      0.000000e+00
name                         4.182102e-07
industry                     4.042741e-02
country                      3.274875e-01
year founded                 5.028253e-01
dtype: float64
In [9]:
def drop_duplicates(df):
    old = df.shape[0]
    df.drop_duplicates(inplace=True)
    new = df.shape[0]
    count = old - new
    if (count == 0):
        print("No duplicate rows were found.")
    else:
        print(f"{count} duplicate rows were found and removed.")
#user-defined function for finding and removing duplicate values, as well as displaying the count
In [10]:
drop_duplicates(df)

828 duplicate rows were found and removed.

/var/folders/p9/wv3jwcsd19x8pf3r2kfnj0580000gn/T/ipykernel_62892/2148154982.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df.drop_duplicates(inplace=True)

Beautiful function that will help us in understanding the missing values in the dataset¶

In [11]:
import missingno as mn# its a 
mn.matrix(df)
Out[11]:
<AxesSubplot:>
In [12]:
import warnings
from pandas.core.common import SettingWithCopyWarning
warnings.simplefilter(action="ignore", category=SettingWithCopyWarning)
In [13]:
df['country'].fillna('missing',inplace = True) 
#filling the Null values with missing country
df['industry'].fillna('missing',inplace = True)
#filling the Null values with missing industry
In [14]:
df.isna().sum()
#again checking the missing values
Out[14]:
name                               3
industry                           0
country                            0
size range                         0
current employee estimate          0
total employee estimate            0
year founded                 3606416
dtype: int64
In [15]:
df['name'].fillna('Unknown',inplace = True)
#filling the na values with Unknown
In [16]:
import matplotlib.pyplot as plt
import seaborn as sns
#importing the necessary libraries for graph plotting
In [17]:
df['year founded'].describe()
#figuring how to handing missing values in year founded
Out[17]:
count    3.566182e+06
mean     2.001746e+03
std      2.096663e+01
min      1.451000e+03
25%      1.999000e+03
50%      2.009000e+03
75%      2.013000e+03
max      2.103000e+03
Name: year founded, dtype: float64
In [18]:
df['year founded'].mean()
Out[18]:
2001.7463059933564
In [19]:
df['year founded'].median()
Out[19]:
2009.0
In [20]:
df['year founded'].mode()
Out[20]:
0    2015.0
Name: year founded, dtype: float64
In [21]:
sns.distplot(df['year founded'])
#distplot helped us in determining the best method for dealing with missing values.
Out[21]:
<AxesSubplot:xlabel='year founded', ylabel='Density'>
In [22]:
df.fillna(df['year founded'].median(),inplace=True)
#median holds good in this case so filling na values with median
In [23]:
mn.matrix(df)
#again checking the missing values in dataset(there is no missing values, now we are good to analyse)
Out[23]:
<AxesSubplot:>

Data Visualization¶

In [24]:
plt.scatter(x= 'year founded',y='total employee estimate',data=df,c='g',label='Total Company Strength')
plt.scatter(x= 'year founded',y='current employee estimate',data=df,c='r',marker='+',label='current Company Strength')
plt.legend(title='Number of employee')
plt.xlabel('Year')
plt.ylabel('Current & total employee')
Out[24]:
Text(0, 0.5, 'Current & total employee')

The graph above depicts the total employee estimate as well as the current employee estimate.

In [25]:
plt.figure
df['year founded'].plot(kind='hist')
Out[25]:
<AxesSubplot:ylabel='Frequency'>

The graph above shows that the majority of the companies were founded between 1920 and 2021.

In [26]:
cor=df.corr()
#To find the pairwise correlation of all columns in the Pandas Dataframe in Python.

sns.heatmap(cor,xticklabels=cor.columns,yticklabels=cor.columns,annot=True)
#To find both linear and nonlinear relationships between variables.
Out[26]:
<AxesSubplot:>
In [27]:
df[df['country']=='india']
#displaying all indian companies
Out[27]:
name industry country size range current employee estimate total employee estimate year founded
1 tata consultancy services information technology and services india 10001+ 190771 341369 1968.0
13 infosys information technology and services india 10001+ 104752 215718 1981.0
26 wipro technologies information technology and services india 10001+ 78261 177368 1945.0
44 hcl technologies information technology and services india 10001+ 59993 127468 1991.0
84 tech mahindra information technology and services india 10001+ 38631 81583 1986.0
... ... ... ... ... ... ... ...
7173085 inodai waldorf school - india education management india 1 - 10 0 1 2009.0
7173100 art infosystems pvt ltd information technology and services india 1 - 10 0 2 2009.0
7173177 data solution provider computer software india 1 - 10 0 4 2009.0
7173330 ginni tea food & beverages india 1 - 10 0 1 2009.0
7173361 red fox consultants pvt. ltd. management consulting india 1 - 10 0 2 2010.0

144439 rows × 7 columns

In [28]:
df[df['country']=='india'].shape
#1,44,439 are Indian companies
Out[28]:
(144439, 7)
In [29]:
df[df['country']=='india']
#displaying all indian companies
Out[29]:
name industry country size range current employee estimate total employee estimate year founded
1 tata consultancy services information technology and services india 10001+ 190771 341369 1968.0
13 infosys information technology and services india 10001+ 104752 215718 1981.0
26 wipro technologies information technology and services india 10001+ 78261 177368 1945.0
44 hcl technologies information technology and services india 10001+ 59993 127468 1991.0
84 tech mahindra information technology and services india 10001+ 38631 81583 1986.0
... ... ... ... ... ... ... ...
7173085 inodai waldorf school - india education management india 1 - 10 0 1 2009.0
7173100 art infosystems pvt ltd information technology and services india 1 - 10 0 2 2009.0
7173177 data solution provider computer software india 1 - 10 0 4 2009.0
7173330 ginni tea food & beverages india 1 - 10 0 1 2009.0
7173361 red fox consultants pvt. ltd. management consulting india 1 - 10 0 2 2010.0

144439 rows × 7 columns

In [30]:
df[df['year founded']>=2015]
#displaying companies that were founded in 2015 or later
Out[30]:
name industry country size range current employee estimate total employee estimate year founded
123 dxc technology information technology and services united states 10001+ 31671 39585 2017.0
214 thales defense & space united states 10001+ 20983 54041 2015.0
218 iqvia hospital & health care united states 10001+ 20957 44812 2016.0
277 lafargeholcim building materials switzerland 10001+ 17859 34272 2015.0
336 upwork internet united states 10001+ 15693 20194 2015.0
... ... ... ... ... ... ... ...
7173266 game slate publishing united states 1 - 10 0 2 2016.0
7173349 anwar real estate inc. real estate missing 1 - 10 0 1 2015.0
7173369 winove wine and spirits portugal 1 - 10 0 2 2015.0
7173402 woonartikel.nl online media netherlands 1 - 10 0 1 2015.0
7173411 fuller, hadeed, & ros-planas, pllc law practice united states 1 - 10 0 2 2015.0

604166 rows × 7 columns

In [31]:
df[df['year founded']<1900]
#displaying companies that were founded before 1900
Out[31]:
name industry country size range current employee estimate total employee estimate year founded
3 us army military united states 10001+ 162163 445958 1800.0
9 at&t telecommunications united states 10001+ 115188 269659 1876.0
15 citi financial services united states 10001+ 101482 298171 1812.0
17 jpmorgan chase & co. financial services united states 10001+ 95234 242135 1823.0
21 siemens electrical/electronic manufacturing germany 10001+ 87381 240528 1847.0
... ... ... ... ... ... ... ...
7166264 athletics northern territory sports missing 1 - 10 0 5 1891.0
7166941 griffin stringer ltd environmental services united kingdom 1 - 10 0 1 1836.0
7168246 institut archéologique du luxembourg asbl government administration belgium 1 - 10 0 1 1847.0
7170784 advokatfirmaet lars meilvang legal services denmark 1 - 10 0 1 1899.0
7173367 city of grenada paper & forest products united states 1 - 10 0 1 1836.0

30000 rows × 7 columns

In [32]:
sort = sorted(set(df.industry.tolist()))
sort
#return all the distinct industries in a sorted order
Out[32]:
['accounting',
 'airlines/aviation',
 'alternative dispute resolution',
 'alternative medicine',
 'animation',
 'apparel & fashion',
 'architecture & planning',
 'arts and crafts',
 'automotive',
 'aviation & aerospace',
 'banking',
 'biotechnology',
 'broadcast media',
 'building materials',
 'business supplies and equipment',
 'capital markets',
 'chemicals',
 'civic & social organization',
 'civil engineering',
 'commercial real estate',
 'computer & network security',
 'computer games',
 'computer hardware',
 'computer networking',
 'computer software',
 'construction',
 'consumer electronics',
 'consumer goods',
 'consumer services',
 'cosmetics',
 'dairy',
 'defense & space',
 'design',
 'e-learning',
 'education management',
 'electrical/electronic manufacturing',
 'entertainment',
 'environmental services',
 'events services',
 'executive office',
 'facilities services',
 'farming',
 'financial services',
 'fine art',
 'fishery',
 'food & beverages',
 'food production',
 'fund-raising',
 'furniture',
 'gambling & casinos',
 'glass, ceramics & concrete',
 'government administration',
 'government relations',
 'graphic design',
 'health, wellness and fitness',
 'higher education',
 'hospital & health care',
 'hospitality',
 'human resources',
 'import and export',
 'individual & family services',
 'industrial automation',
 'information services',
 'information technology and services',
 'insurance',
 'international affairs',
 'international trade and development',
 'internet',
 'investment banking',
 'investment management',
 'judiciary',
 'law enforcement',
 'law practice',
 'legal services',
 'legislative office',
 'leisure, travel & tourism',
 'libraries',
 'logistics and supply chain',
 'luxury goods & jewelry',
 'machinery',
 'management consulting',
 'maritime',
 'market research',
 'marketing and advertising',
 'mechanical or industrial engineering',
 'media production',
 'medical devices',
 'medical practice',
 'mental health care',
 'military',
 'mining & metals',
 'missing',
 'motion pictures and film',
 'museums and institutions',
 'music',
 'nanotechnology',
 'newspapers',
 'non-profit organization management',
 'nonprofit organization management',
 'oil & energy',
 'online media',
 'outsourcing/offshoring',
 'package/freight delivery',
 'packaging and containers',
 'paper & forest products',
 'performing arts',
 'pharmaceuticals',
 'philanthropy',
 'photography',
 'plastics',
 'political organization',
 'primary/secondary education',
 'printing',
 'professional training & coaching',
 'program development',
 'public policy',
 'public relations and communications',
 'public safety',
 'publishing',
 'railroad manufacture',
 'ranching',
 'real estate',
 'recreational facilities and services',
 'religious institutions',
 'renewables & environment',
 'research',
 'restaurants',
 'retail',
 'security and investigations',
 'semiconductors',
 'shipbuilding',
 'sporting goods',
 'sports',
 'staffing and recruiting',
 'supermarkets',
 'telecommunications',
 'textiles',
 'think tanks',
 'tobacco',
 'translation and localization',
 'transportation/trucking/railroad',
 'utilities',
 'venture capital & private equity',
 'veterinary',
 'warehousing',
 'wholesale',
 'wine and spirits',
 'wireless',
 'writing and editing']
In [33]:
df['industry'].nunique()
#will return no of unique industry
Out[33]:
149
In [34]:
IT_industries = [
 'animation',
 'biotechnology',
 'computer & network security',
 'computer games',
 'computer hardware',
 'computer networking',
 'computer software',
 'consumer electronics',
 'defense & space',
 'e-learning',
 'industrial automation',
 'information services',
 'information technology and services',
 'internet',
 'mechanical or industrial engineering',
 'program development',
 'telecommunications',
 'wireless'
]
#grouped all domains under IT industry
In [35]:
Top_it = df[df.industry.isin(IT_industries)]
#The domains listed in IT industries are checked in the industry column.
In [36]:
it_cities = Top_it[df.country != 'missing'].country.value_counts(normalize=True).sort_values(ascending=False)\
            .reset_index().rename(columns={'index':'country_city','country': 'Company_count'})
it_cities.head(10)
#removing the country name missing 
#counting the country and sorting
#renaming 

/var/folders/p9/wv3jwcsd19x8pf3r2kfnj0580000gn/T/ipykernel_62892/617132183.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.
  it_cities = Top_it[df.country != 'missing'].country.value_counts(normalize=True).sort_values(ascending=False)\
Out[36]:
country_city Company_count
0 united states 0.373955
1 united kingdom 0.089612
2 india 0.056632
3 canada 0.037975
4 spain 0.037179
5 france 0.030554
6 brazil 0.030110
7 germany 0.028559
8 netherlands 0.028166
9 italy 0.028036

Top 10 IT companies by country and city

In [37]:
sports=df[df['industry']=='sports']
sports
#displaying the sports industry
Out[37]:
name industry country size range current employee estimate total employee estimate year founded
2026 nothing sports missing 10001+ 4243 5181 2009.0
4273 columbia sportswear sports united states 1001 - 5000 2370 6095 1938.0
4638 img sports united states 1001 - 5000 2202 9909 1960.0
4822 padi sports united states 1001 - 5000 2132 3443 1966.0
5078 fc barcelona sports spain 5001 - 10000 2036 3310 1899.0
... ... ... ... ... ... ... ...
7172831 fc preussen hameln 07 sports germany 1 - 10 0 1 2009.0
7172844 surestream football academy ltd sports united kingdom 1 - 10 0 1 2009.0
7172863 club de golf costa dorada sports missing 1 - 10 0 4 2009.0
7173118 morris county youth soccer association sports united states 1 - 10 0 3 2009.0
7173336 aquatech swim school sports united states 1 - 10 0 4 2009.0

62612 rows × 7 columns

In [38]:
indust = df['industry'].value_counts().rename_axis('industry').reset_index(name='Company_count')
indust.head(10)
#Industry and company_count
Out[38]:
industry Company_count
0 information technology and services 452995
1 marketing and advertising 338110
2 missing 289846
3 construction 257881
4 management consulting 194540
5 real estate 193437
6 computer software 191252
7 internet 172199
8 financial services 170850
9 health, wellness and fitness 145642
In [39]:
indust = df['industry'].value_counts(normalize=True).rename_axis('industry').reset_index(name='Company_count')
indust
Out[39]:
industry Company_count
0 information technology and services 0.063156
1 marketing and advertising 0.047139
2 missing 0.040410
3 construction 0.035954
4 management consulting 0.027123
... ... ...
144 fishery 0.000328
145 ranching 0.000324
146 railroad manufacture 0.000253
147 tobacco 0.000189
148 legislative office 0.000073

149 rows × 2 columns

In [40]:
indust.shape
Out[40]:
(149, 2)

Better way of understanding the dataset by Pandas Profiling¶

image.png

Pandas profiling is an open source Python module with which we can quickly do an exploratory data analysis with just a few lines of code. Besides, if this is not enough to convince us to use this tool, it also generates interactive reports in web format that can be presented to any person, even if they don’t know programming.

In short, what pandas profiling does is save us all the work of visualizing and understanding the distribution of each variable. It generates a report with all the information easily available.

In [41]:
from pandas_profiling import ProfileReport
profile = ProfileReport(df)
profile

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]
Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]
Out[41]:




























Autoviz: Automatically Visualize any Dataset¶






















image.png























AutoViz performs automatic visualization of any dataset with just one line of code. AutoViz can find the most important features and plot impactful visualizations only using those automatically selected features. Also, AutoViz is incredibly fast so it creates visualization within seconds.



















In [42]:



     


#importing Autoviz class
from autoviz.AutoViz_Class import AutoViz_Class
AV = AutoViz_Class()
report = AV.AutoViz('companies_sorted.csv')
report



     






















    
    






Imported v0.1.55. After importing, execute '%matplotlib inline' to display charts in Jupyter.
    AV = AutoViz_Class()
    dfte = AV.AutoViz(filename, sep=',', depVar='', dfte=None, header=0, verbose=1, lowess=False,
               chart_format='svg',max_rows_analyzed=150000,max_cols_analyzed=30, save_plot_dir=None)
Update: verbose=0 displays charts in your local Jupyter notebook.
        verbose=1 additionally provides EDA data cleaning suggestions. It also displays charts.
        verbose=2 does not display charts but saves them in AutoViz_Plots folder in local machine.
        chart_format='bokeh' displays charts in your local Jupyter notebook.
        chart_format='server' displays charts in your browser: one tab for each chart type
        chart_format='html' silently saves interactive HTML files in your local machine










    
    

















    
    

















    
    
















    
    

















    
    

















    
    












  
  


  
  














    
    






    max_rows_analyzed is smaller than dataset shape 7173426...
        randomly sampled 150000 rows from read CSV file
Shape of your Data Set loaded: (150000, 11)
#######################################################################################
######################## C L A S S I F Y I N G  V A R I A B L E S  ####################
#######################################################################################
Classifying variables in data set...










    
    









  
    

       
      Nuniques
      dtype
      Nulls
      Nullpercent
      NuniquePercent
      Value counts Min
      Data cleaning improvement suggestions
    

  
  
    

      Unnamed: 0
      10000
      int64
      0
      0.000000
      100.000000
      0
      possible ID column: drop
    

    

      name
      10000
      object
      0
      0.000000
      100.000000
      1
      combine rare categories, possible ID column: drop
    

    

      linkedin url
      10000
      object
      0
      0.000000
      100.000000
      1
      combine rare categories, possible ID column: drop
    

    

      domain
      7755
      object
      2245
      22.450000
      77.550000
      1
      combine rare categories, fill missing values, fix mixed data types
    

    

      locality
      3186
      object
      3560
      35.600000
      31.860000
      1
      combine rare categories, fill missing values, fix mixed data types
    

    

      total employee estimate
      340
      int64
      0
      0.000000
      3.400000
      0
      
    

    

      current employee estimate
      229
      int64
      0
      0.000000
      2.290000
      0
      
    

    

      industry
      147
      object
      400
      4.000000
      1.470000
      2
      combine rare categories, fill missing values, fix mixed data types
    

    

      year founded
      144
      float64
      5000
      50.000000
      1.440000
      0
      fill missing values, skewed column: cap or drop possible outliers
    

    

      country
      126
      object
      3319
      33.190000
      1.260000
      1
      combine rare categories, fill missing values, fix mixed data types
    

    

      size range
      8
      object
      0
      0.000000
      0.080000
      3
      
    

  












    
    






    11 Predictors classified...
        1 variables removed since they were ID or low-information variables
        List of variables removed: ['Unnamed: 0']
Since Number of Rows in data 150000 exceeds maximum, randomly sampling 150000 rows for EDA...










    
    






[nltk_data] Downloading collection 'popular'
[nltk_data]    | 
[nltk_data]    | Downloading package cmudict to
[nltk_data]    |     /Users/shreyanthhg/nltk_data...
[nltk_data]    |   Package cmudict is already up-to-date!
[nltk_data]    | Downloading package gazetteers to
[nltk_data]    |     /Users/shreyanthhg/nltk_data...
[nltk_data]    |   Package gazetteers is already up-to-date!
[nltk_data]    | Downloading package genesis to
[nltk_data]    |     /Users/shreyanthhg/nltk_data...
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Out[42]:











  
    

      
      Unnamed: 0
      name
      domain
      year founded
      industry
      size range
      locality
      country
      linkedin url
      current employee estimate
      total employee estimate
    

  
  
    

      1712978
      2845651
      valeo networks inc.
      valeonetworks.com
      NaN
      information technology and services
      1 - 10
      scottsdale, arizona, united states
      united states
      linkedin.com/company/valeo-networks-inc-
      4
      6
    

    

      1125233
      2315190
      u5 marketing - bureau de arte
      u5marketing.com.br
      2011.0
      marketing and advertising
      11 - 50
      sao paulo, sao paulo, brazil
      brazil
      linkedin.com/company/u5-marketing---bureau-de-criação
      7
      14
    

    

      2763274
      683115
      star custom framers
      selectsportswear.com
      NaN
      consumer services
      1 - 10
      NaN
      NaN
      linkedin.com/company/star-custom-framers
      2
      2
    

    

      6747208
      2011695
      git hydros consult gmbh
      hydrosconsult.de
      2001.0
      information technology and services
      1 - 10
      NaN
      NaN
      linkedin.com/company/git-hydros-consult-gmbh
      0
      1
    

    

      2587339
      3082914
      receptorpharma
      NaN
      NaN
      pharmaceuticals
      1 - 10
      NaN
      NaN
      linkedin.com/company/receptorpharma
      2
      2
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      6439775
      1899698
      naked jobs
      nakedgenerations.com
      2009.0
      staffing and recruiting
      1 - 10
      london, greater london, united kingdom
      united kingdom
      linkedin.com/company/naked-jobs
      0
      1
    

    

      3217897
      2074113
      sites security
      sites.ua
      NaN
      security and investigations
      1 - 10
      NaN
      NaN
      linkedin.com/company/sites-security
      2
      5
    

    

      12995
      2508743
      buscando recolocacao
      eloead.com.br
      NaN
      capital markets
      1001 - 5000
      NaN
      NaN
      linkedin.com/company/buscando-recolocacao
      876
      1112
    

    

      3283722
      4588874
      exclusive assistant group
      askeag.com
      2010.0
      management consulting
      1 - 10
      miami, florida, united states
      united states
      linkedin.com/company/exclusive-assistant-group
      1
      1
    

    

      1152002
      2822990
      realty connection llc
      realtyconnection.com
      2012.0
      internet
      11 - 50
      lakewood, colorado, united states
      united states
      linkedin.com/company/realty-connection-llc
      6
      11
    

  



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Conclusion¶






















    

  1. 'United States', 'United Kingdom', 'Canada', 'India', and ‘Spain’ are top 5 countries with the highest number of companies.
    2. 

  2. It shows that majority of the companies were founded between 1920 to 2021.
    3. 

  3. There are 1,44,439 Indian companies.
    4. 

  4. In total of 71,72,598 in the world 6,04,166 companies where founded after 2015 will covers 8% of total list.
    5. 

  5. There were 30k company which was established before 1900. Which includes Us army, JPMorgan chase & co., athletics northern territory, city of Grenada.
    6. 

  6. 149 different types industries are present, which includes, Information technology and services (.06%), marketing and advertising (.04), construction (.04%), management consulting (.02).
    7. 

  7. United States has 37% of companies, where has India .05 % of companies.
    8. 




















In [ ]: